Setting up data
Data from Human Mortality Database and Human Fertility Database was used to calculate birth rates. This was tidied to give Under-18 rates:
| 1 |
Austria |
1985 |
8.63 |
9172.10 |
1.04 |
0.13 |
64.31 |
| 1 |
Austria |
1986 |
8.44 |
13083.07 |
1.04 |
0.25 |
64.04 |
| 1 |
Austria |
1987 |
8.19 |
16392.77 |
1.03 |
0.34 |
63.77 |
| 1 |
Austria |
1988 |
7.89 |
17578.62 |
1.04 |
0.48 |
63.50 |
| 1 |
Austria |
1989 |
8.58 |
17468.95 |
1.04 |
0.66 |
63.23 |
| 1 |
Austria |
1990 |
8.80 |
21680.99 |
1.04 |
0.95 |
62.96 |
Abortion estimates were added to give Under-20 rates:
| 2 |
Czechia |
1990 |
71.17 |
46.01 |
3917.16 |
1.05 |
0.00 |
75.22 |
| 2 |
Czechia |
1991 |
72.91 |
47.52 |
2878.72 |
1.05 |
0.01 |
75.16 |
| 2 |
Czechia |
1992 |
69.70 |
45.14 |
3352.03 |
1.05 |
0.04 |
75.03 |
| 2 |
Czechia |
1993 |
62.31 |
42.91 |
3931.74 |
1.05 |
0.14 |
74.90 |
| 2 |
Czechia |
1994 |
46.97 |
32.33 |
4601.95 |
1.04 |
0.29 |
74.77 |
| 2 |
Czechia |
1995 |
36.53 |
24.43 |
5788.15 |
1.04 |
0.47 |
74.64 |
Iterating through year combinations
For each comparison, I iterated through all combinations of years as special predictors to minimise MSPE (whilst prioritising fewest groupings). For example, for the under-18 basic model with years as special predictors:
it_u18_rateSp <- testSynthIterations(
yrs = 1985:1998,
pred = "rate",
data = synthData_u18[,1:4],
ccodes = u_18_ccodes,
n = 4,
predictors = NULL,
time.optimise = 1985:1998
) %>%
arrange(groups, mspe)
Generating Synthetic Control models for all Under-18 comparisons
Model 1: Rate only as predictor
England vs Synthetic Control
Weights
| Lithuania |
0.648 |
| Italy |
0.179 |
| Norway |
0.105 |
| United States of America |
0.056 |
| Poland |
0.005 |
| Czechia |
0.001 |
| Estonia |
0.001 |
| Finland |
0.001 |
| Germany |
0.001 |
| Portugal |
0.001 |
| Switzerland |
0.001 |
| Austria |
0.000 |
| Denmark |
0.000 |
| France |
0.000 |
| Hungary |
0.000 |
| Iceland |
0.000 |
| Netherlands |
0.000 |
| Northern Ireland |
0.000 |
| Scotland |
0.000 |
| Slovenia |
0.000 |
| Spain |
0.000 |
| Sweden |
0.000 |
| special.rate.1985.1987 |
0.071 |
| special.rate.1988.1989 |
0.221 |
| special.rate.1990.1992 |
0.615 |
| special.rate.1993.1998 |
0.092 |
Placebo testing by country and time
Model 2: GDP as predictor
England vs Synthetic Control
Weights
| Norway |
0.449 |
| United States of America |
0.346 |
| Sweden |
0.193 |
| Austria |
0.002 |
| Germany |
0.002 |
| Netherlands |
0.002 |
| Denmark |
0.001 |
| France |
0.001 |
| Iceland |
0.001 |
| Italy |
0.001 |
| Portugal |
0.001 |
| Spain |
0.001 |
| Switzerland |
0.001 |
| Finland |
0.000 |
| GDPperCap |
0.819 |
| special.rate.1985.1994 |
0.062 |
| special.rate.1995.1998 |
0.119 |
Placebo testing by country and time
Model 3: All predictors
England vs Synthetic Control
Weights
| Netherlands |
0.437 |
| United States of America |
0.247 |
| Sweden |
0.194 |
| Germany |
0.106 |
| Finland |
0.002 |
| France |
0.002 |
| Iceland |
0.002 |
| Italy |
0.002 |
| Norway |
0.002 |
| Switzerland |
0.002 |
| Austria |
0.001 |
| Portugal |
0.001 |
| Spain |
0.001 |
| Denmark |
0.000 |
| GDPperCap |
0.132 |
| MobilePhones |
0 |
| UrbanPop |
0.15 |
| MF_ratio |
0.255 |
| special.rate.1985.1994 |
0.047 |
| special.rate.1995.1998 |
0.415 |
Placebo testing by country and time
Generating Synthetic Control models for all Under-20 comparisons
Model 4: England vs Synthetic Control
England vs Synthetic Control
Weights
| Italy |
0.723 |
| United States of America |
0.211 |
| Netherlands |
0.066 |
| Czechia |
0.000 |
| Denmark |
0.000 |
| Estonia |
0.000 |
| Finland |
0.000 |
| France |
0.000 |
| Germany |
0.000 |
| Hungary |
0.000 |
| Iceland |
0.000 |
| Lithuania |
0.000 |
| New Zealand |
0.000 |
| Norway |
0.000 |
| Poland |
0.000 |
| Portugal |
0.000 |
| Scotland |
0.000 |
| Slovenia |
0.000 |
| Spain |
0.000 |
| Sweden |
0.000 |
| Switzerland |
0.000 |
| special.pRate.1990.1993 |
0.212 |
| special.pRate.1994.1996 |
0.607 |
| special.pRate.1997.1998 |
0.181 |
Placebo testing by country and time
Model 5: GDP as predictor
England vs Synthetic Control
Weights
| Lithuania |
0.581 |
| United States of America |
0.419 |
| Czechia |
0.000 |
| Denmark |
0.000 |
| Finland |
0.000 |
| France |
0.000 |
| Germany |
0.000 |
| Hungary |
0.000 |
| Iceland |
0.000 |
| Italy |
0.000 |
| Netherlands |
0.000 |
| New Zealand |
0.000 |
| Norway |
0.000 |
| Poland |
0.000 |
| Portugal |
0.000 |
| Spain |
0.000 |
| Sweden |
0.000 |
| Switzerland |
0.000 |
| GDPperCap |
0 |
| special.pRate.1990.1994 |
0.654 |
| special.pRate.1995.1998 |
0.346 |
Placebo testing by country and time
Model 6: All predictors
England vs Synthetic Control
Weights
| Lithuania |
0.581 |
| United States of America |
0.419 |
| Czechia |
0.000 |
| Denmark |
0.000 |
| Finland |
0.000 |
| France |
0.000 |
| Germany |
0.000 |
| Hungary |
0.000 |
| Iceland |
0.000 |
| Italy |
0.000 |
| Netherlands |
0.000 |
| Norway |
0.000 |
| Portugal |
0.000 |
| Spain |
0.000 |
| Sweden |
0.000 |
| Switzerland |
0.000 |
| GDPperCap |
0 |
| MobilePhones |
0 |
| UrbanPop |
0 |
| MF_ratio |
0 |
| special.pRate.1990.1994 |
0.652 |
| special.pRate.1995.1998 |
0.347 |
Placebo testing by country and time